Abstract of "Expert systems for forecasting,"
Fred Collopy, The Weatherhead School, Case Western Reserve University, J. Scott Armstrong,
The Wharton School, University of Pennsylvania, and Monica Adya, Department of Information
Systems, University of Maryland at Baltimore

Expert systems use rules to represent expertsí reasoning in solving problems. The rules are based on knowledge about methods and the problem domain. To acquire knowledge for an expert system, one should rely on a variety of
sources, such as textbooks, research papers, interviews, surveys, and protocol analysis. Protocol analysis is especially useful if the area to be modeled is complex or if experts lack an awareness of their processes.
Expert systems should be easy to use, incorporate the best available knowledge, and reveal the reasoning behind the recommendations they make. In forecasting, the most promising applications of expert systems are to
replace unaided judgment in cases requiring many forecasts, to model complex problems where data on the dependent variable are of poor quality, and to handle semi-structured problems. We found 15 comparisons of forecast validity involving expert systems. As expected, expert systems were more accurate than unaided judgment, six comparisons to one, with one tie. Expert systems were less accurate than judgmental bootstrapping in two comparisons with two ties. There was little evidence with which to compare expert systems and econometric models; expert systems were better in one study and tied in two.